Number of views. Tab. REF  shows the performance with different numbers of view on Nr3D [1]}. We first compare the performance of 4-view training but test in different numbers of views, which shows that the improvement of MVT comes from two parts. Firstly, when training with 4 views but testing with 1 view, the overall accuracy \(51.6\%\)  is much higher than training in 1 view only, i.e., \(46.2\%\) , and the “View-dep.” accuracy also increases by \(8.1\%\) . This result shows that multi-view modeling can learn an effective representation that can benefit different views. Secondly, when increasing the view number during testing, the grounding accuracy can still be improved. This observation tells that the information aggregation from the multi-view space can be helpful. We also compare the results of training with other view numbers. When training with only 2 views, the model's performance can achieve \(51.9\%\) .
When two views are relatively close, the position information provided by them for visual grounding is similar and redundant, thus we found 4-view can already provide a robust multi-view representation. When training with more views (e.g., \(N=8\)  views), redundant views cannot improve performance continually and may cause a little performance degradation due to low training efficiency.
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